Overview

Dataset statistics

Number of variables12
Number of observations1475
Missing cells11
Missing cells (%)0.1%
Duplicate rows120
Duplicate rows (%)8.1%
Total size in memory213.4 KiB
Average record size in memory148.1 B

Variable types

Categorical2
Numeric10

Alerts

Dataset has 120 (8.1%) duplicate rowsDuplicates
System is highly imbalanced (99.2%)Imbalance
NOC is highly skewed (γ1 = 36.80615842)Skewed
CBO is highly skewed (γ1 = 23.84549055)Skewed
fan-in is highly skewed (γ1 = 23.55586028)Skewed
LOC is highly skewed (γ1 = 21.12101708)Skewed
WMC has 28 (1.9%) zerosZeros
NOC has 1301 (88.2%) zerosZeros
CBO has 223 (15.1%) zerosZeros
LCOM has 469 (31.8%) zerosZeros
fan-in has 438 (29.7%) zerosZeros
fan-out has 382 (25.9%) zerosZeros
LOC has 26 (1.8%) zerosZeros
MAXCC has 104 (7.1%) zerosZeros
AVGCC has 104 (7.1%) zerosZeros
BUGS has 892 (60.5%) zerosZeros

Reproduction

Analysis started2024-04-03 00:30:15.345879
Analysis finished2024-04-03 00:30:30.616265
Duration15.27 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

System
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size86.6 KiB
ant
1474 
------WebKitFormBoundaryIaolhmaqDHSCmiac--
 
1

Length

Max length42
Median length3
Mean length3.0264407
Min length3

Characters and Unicode

Total characters4464
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowant
2nd rowant
3rd rowant
4th rowant
5th rowant

Common Values

ValueCountFrequency (%)
ant 1474
99.9%
------WebKitFormBoundaryIaolhmaqDHSCmiac-- 1
 
0.1%

Length

2024-04-03T01:30:30.774800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T01:30:31.027932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ant 1474
99.9%
webkitformboundaryiaolhmaqdhscmiac 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1478
33.1%
t 1475
33.0%
n 1475
33.0%
- 8
 
0.2%
o 3
 
0.1%
m 3
 
0.1%
i 2
 
< 0.1%
r 2
 
< 0.1%
I 1
 
< 0.1%
C 1
 
< 0.1%
Other values (16) 16
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4447
99.6%
Uppercase Letter 9
 
0.2%
Dash Punctuation 8
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1478
33.2%
t 1475
33.2%
n 1475
33.2%
o 3
 
0.1%
m 3
 
0.1%
i 2
 
< 0.1%
r 2
 
< 0.1%
q 1
 
< 0.1%
h 1
 
< 0.1%
l 1
 
< 0.1%
Other values (6) 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
I 1
11.1%
C 1
11.1%
S 1
11.1%
H 1
11.1%
D 1
11.1%
B 1
11.1%
F 1
11.1%
K 1
11.1%
W 1
11.1%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4456
99.8%
Common 8
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1478
33.2%
t 1475
33.1%
n 1475
33.1%
o 3
 
0.1%
m 3
 
0.1%
i 2
 
< 0.1%
r 2
 
< 0.1%
I 1
 
< 0.1%
C 1
 
< 0.1%
S 1
 
< 0.1%
Other values (15) 15
 
0.3%
Common
ValueCountFrequency (%)
- 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1478
33.1%
t 1475
33.0%
n 1475
33.0%
- 8
 
0.2%
o 3
 
0.1%
m 3
 
0.1%
i 2
 
< 0.1%
r 2
 
< 0.1%
I 1
 
< 0.1%
C 1
 
< 0.1%
Other values (16) 16
 
0.4%

WMC
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)3.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean12.080733
Minimum0
Maximum511
Zeros28
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:31.260888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q311
95-th percentile38
Maximum511
Range511
Interquartile range (IQR)8

Descriptive statistics

Standard deviation30.556453
Coefficient of variation (CV)2.5293543
Kurtosis132.88885
Mean12.080733
Median Absolute Deviation (MAD)3
Skewness10.030238
Sum17807
Variance933.6968
MonotonicityNot monotonic
2024-04-03T01:30:31.485413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 212
14.4%
2 201
13.6%
3 158
10.7%
4 138
9.4%
1 106
 
7.2%
10 77
 
5.2%
7 74
 
5.0%
12 71
 
4.8%
8 68
 
4.6%
13 62
 
4.2%
Other values (44) 307
20.8%
ValueCountFrequency (%)
0 28
 
1.9%
1 106
7.2%
2 201
13.6%
3 158
10.7%
4 138
9.4%
5 212
14.4%
7 74
 
5.0%
8 68
 
4.6%
10 77
 
5.2%
11 52
 
3.5%
ValueCountFrequency (%)
511 2
 
0.1%
411 1
 
0.1%
311 2
 
0.1%
211 4
 
0.3%
131 1
 
0.1%
127 1
 
0.1%
111 18
1.2%
100 1
 
0.1%
78 1
 
0.1%
77 1
 
0.1%

DIT
Categorical

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size86.6 KiB
1.0
824 
2.0
218 
4.0
205 
3.0
199 
5.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4422
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 824
55.9%
2.0 218
 
14.8%
4.0 205
 
13.9%
3.0 199
 
13.5%
5.0 28
 
1.9%
(Missing) 1
 
0.1%

Length

2024-04-03T01:30:31.726772image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T01:30:31.931927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 824
55.9%
2.0 218
 
14.8%
4.0 205
 
13.9%
3.0 199
 
13.5%
5.0 28
 
1.9%

Most occurring characters

ValueCountFrequency (%)
. 1474
33.3%
0 1474
33.3%
1 824
18.6%
2 218
 
4.9%
4 205
 
4.6%
3 199
 
4.5%
5 28
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2948
66.7%
Other Punctuation 1474
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1474
50.0%
1 824
28.0%
2 218
 
7.4%
4 205
 
7.0%
3 199
 
6.8%
5 28
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 1474
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4422
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1474
33.3%
0 1474
33.3%
1 824
18.6%
2 218
 
4.9%
4 205
 
4.6%
3 199
 
4.5%
5 28
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1474
33.3%
0 1474
33.3%
1 824
18.6%
2 218
 
4.9%
4 205
 
4.6%
3 199
 
4.5%
5 28
 
0.6%

NOC
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)0.9%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.71913161
Minimum0
Maximum511
Zeros1301
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:32.160031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum511
Range511
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.497321
Coefficient of variation (CV)18.768916
Kurtosis1389.7765
Mean0.71913161
Median Absolute Deviation (MAD)0
Skewness36.806158
Sum1060
Variance182.17768
MonotonicityNot monotonic
2024-04-03T01:30:32.605566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 1301
88.2%
1 84
 
5.7%
2 36
 
2.4%
3 22
 
1.5%
5 11
 
0.7%
7 4
 
0.3%
4 3
 
0.2%
8 3
 
0.2%
10 3
 
0.2%
35 2
 
0.1%
Other values (4) 5
 
0.3%
ValueCountFrequency (%)
0 1301
88.2%
1 84
 
5.7%
2 36
 
2.4%
3 22
 
1.5%
4 3
 
0.2%
5 11
 
0.7%
7 4
 
0.3%
8 3
 
0.2%
10 3
 
0.2%
11 2
 
0.1%
ValueCountFrequency (%)
511 1
 
0.1%
52 1
 
0.1%
35 2
 
0.1%
34 1
 
0.1%
11 2
 
0.1%
10 3
 
0.2%
8 3
 
0.2%
7 4
 
0.3%
5 11
0.7%
4 3
 
0.2%

CBO
Real number (ℝ)

SKEWED  ZEROS 

Distinct50
Distinct (%)3.4%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean8.7462687
Minimum0
Maximum1111
Zeros223
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:32.904819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q38
95-th percentile25
Maximum1111
Range1111
Interquartile range (IQR)6

Descriptive statistics

Standard deviation34.667631
Coefficient of variation (CV)3.9637052
Kurtosis712.05866
Mean8.7462687
Median Absolute Deviation (MAD)3
Skewness23.845491
Sum12892
Variance1201.8446
MonotonicityNot monotonic
2024-04-03T01:30:33.151821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 223
15.1%
5 209
14.2%
2 189
12.8%
3 170
11.5%
4 117
7.9%
1 79
 
5.4%
10 72
 
4.9%
7 71
 
4.8%
11 62
 
4.2%
8 57
 
3.9%
Other values (40) 225
15.3%
ValueCountFrequency (%)
0 223
15.1%
1 79
 
5.4%
2 189
12.8%
3 170
11.5%
4 117
7.9%
5 209
14.2%
7 71
 
4.8%
8 57
 
3.9%
10 72
 
4.9%
11 62
 
4.2%
ValueCountFrequency (%)
1111 1
 
0.1%
411 1
 
0.1%
311 1
 
0.1%
211 2
 
0.1%
125 1
 
0.1%
111 7
0.5%
105 1
 
0.1%
87 1
 
0.1%
83 1
 
0.1%
80 1
 
0.1%

LCOM
Real number (ℝ)

ZEROS 

Distinct169
Distinct (%)11.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean112.96744
Minimum0
Maximum18211
Zeros469
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:33.461924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q328
95-th percentile425.55
Maximum18211
Range18211
Interquartile range (IQR)28

Descriptive statistics

Standard deviation675.5484
Coefficient of variation (CV)5.9800278
Kurtosis397.50235
Mean112.96744
Median Absolute Deviation (MAD)3
Skewness17.409187
Sum166514
Variance456365.64
MonotonicityNot monotonic
2024-04-03T01:30:33.704044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 469
31.8%
1 175
 
11.9%
3 91
 
6.2%
5 81
 
5.5%
10 60
 
4.1%
13 54
 
3.7%
21 44
 
3.0%
4 32
 
2.2%
55 27
 
1.8%
28 20
 
1.4%
Other values (159) 421
28.5%
ValueCountFrequency (%)
0 469
31.8%
1 175
 
11.9%
2 16
 
1.1%
3 91
 
6.2%
4 32
 
2.2%
5 81
 
5.5%
7 5
 
0.3%
8 8
 
0.5%
10 60
 
4.1%
11 14
 
0.9%
ValueCountFrequency (%)
18211 1
0.1%
10111 1
0.1%
7135 1
0.1%
5137 1
0.1%
5115 2
0.1%
4811 1
0.1%
2870 1
0.1%
2700 1
0.1%
2543 1
0.1%
2445 1
0.1%

fan-in
Real number (ℝ)

SKEWED  ZEROS 

Distinct42
Distinct (%)2.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5.1071913
Minimum0
Maximum1111
Zeros438
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:33.970973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile13
Maximum1111
Range1111
Interquartile range (IQR)3

Descriptive statistics

Standard deviation35.817606
Coefficient of variation (CV)7.0131711
Kurtosis659.43836
Mean5.1071913
Median Absolute Deviation (MAD)1
Skewness23.55586
Sum7528
Variance1282.9009
MonotonicityNot monotonic
2024-04-03T01:30:34.241253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 448
30.4%
0 438
29.7%
2 169
 
11.5%
3 93
 
6.3%
5 80
 
5.4%
4 51
 
3.5%
7 34
 
2.3%
10 29
 
2.0%
13 28
 
1.9%
8 18
 
1.2%
Other values (32) 86
 
5.8%
ValueCountFrequency (%)
0 438
29.7%
1 448
30.4%
2 169
 
11.5%
3 93
 
6.3%
4 51
 
3.5%
5 80
 
5.4%
7 34
 
2.3%
8 18
 
1.2%
10 29
 
2.0%
11 17
 
1.2%
ValueCountFrequency (%)
1111 1
0.1%
511 1
0.1%
411 1
0.1%
311 1
0.1%
211 1
0.1%
118 1
0.1%
111 1
0.1%
104 1
0.1%
82 1
0.1%
78 1
0.1%

fan-out
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)1.8%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.4321574
Minimum0
Maximum311
Zeros382
Zeros (%)25.9%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:34.465144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile12
Maximum311
Range311
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.690613
Coefficient of variation (CV)3.540175
Kurtosis243.9858
Mean4.4321574
Median Absolute Deviation (MAD)2
Skewness14.367664
Sum6533
Variance246.19533
MonotonicityNot monotonic
2024-04-03T01:30:34.751421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 382
25.9%
1 252
17.1%
2 196
13.3%
3 177
12.0%
4 109
 
7.4%
5 108
 
7.3%
7 53
 
3.6%
8 45
 
3.1%
11 31
 
2.1%
10 31
 
2.1%
Other values (17) 90
 
6.1%
ValueCountFrequency (%)
0 382
25.9%
1 252
17.1%
2 196
13.3%
3 177
12.0%
4 109
 
7.4%
5 108
 
7.3%
7 53
 
3.6%
8 45
 
3.1%
10 31
 
2.1%
11 31
 
2.1%
ValueCountFrequency (%)
311 2
 
0.1%
211 2
 
0.1%
111 5
0.3%
47 1
 
0.1%
37 2
 
0.1%
30 1
 
0.1%
27 3
0.2%
26 2
 
0.1%
25 5
0.3%
24 2
 
0.1%

LOC
Real number (ℝ)

SKEWED  ZEROS 

Distinct401
Distinct (%)27.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1071.7537
Minimum0
Maximum241111
Zeros26
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:35.005826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median75
Q3287.75
95-th percentile2646
Maximum241111
Range241111
Interquartile range (IQR)274.75

Descriptive statistics

Standard deviation8715.7348
Coefficient of variation (CV)8.1322178
Kurtosis518.98946
Mean1071.7537
Median Absolute Deviation (MAD)68
Skewness21.121017
Sum1579765
Variance75964034
MonotonicityNot monotonic
2024-04-03T01:30:35.335455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 60
 
4.1%
1 45
 
3.1%
13 44
 
3.0%
5 42
 
2.8%
4 34
 
2.3%
3 34
 
2.3%
12 33
 
2.2%
28 31
 
2.1%
20 26
 
1.8%
0 26
 
1.8%
Other values (391) 1099
74.5%
ValueCountFrequency (%)
0 26
1.8%
1 45
3.1%
2 60
4.1%
3 34
2.3%
4 34
2.3%
5 42
2.8%
7 14
 
0.9%
8 17
 
1.2%
10 20
 
1.4%
11 16
 
1.1%
ValueCountFrequency (%)
241111 1
0.1%
181111 1
0.1%
71811 1
0.1%
51125 1
0.1%
51113 1
0.1%
51111 1
0.1%
35112 1
0.1%
31118 1
0.1%
31111 1
0.1%
25112 1
0.1%

MAXCC
Real number (ℝ)

ZEROS 

Distinct38
Distinct (%)2.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.2584803
Minimum0
Maximum311
Zeros104
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:35.518499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile13
Maximum311
Range311
Interquartile range (IQR)2

Descriptive statistics

Standard deviation13.691205
Coefficient of variation (CV)3.2150448
Kurtosis226.53105
Mean4.2584803
Median Absolute Deviation (MAD)0
Skewness12.904443
Sum6277
Variance187.4491
MonotonicityNot monotonic
2024-04-03T01:30:35.800006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 782
53.0%
2 129
 
8.7%
0 104
 
7.1%
3 100
 
6.8%
5 88
 
6.0%
4 59
 
4.0%
10 35
 
2.4%
7 35
 
2.4%
11 25
 
1.7%
13 22
 
1.5%
Other values (28) 95
 
6.4%
ValueCountFrequency (%)
0 104
 
7.1%
1 782
53.0%
2 129
 
8.7%
3 100
 
6.8%
4 59
 
4.0%
5 88
 
6.0%
7 35
 
2.4%
8 20
 
1.4%
10 35
 
2.4%
11 25
 
1.7%
ValueCountFrequency (%)
311 1
 
0.1%
211 1
 
0.1%
127 2
 
0.1%
111 5
0.3%
57 1
 
0.1%
55 1
 
0.1%
53 1
 
0.1%
52 1
 
0.1%
51 1
 
0.1%
45 1
 
0.1%

AVGCC
Real number (ℝ)

ZEROS 

Distinct290
Distinct (%)19.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.2252294
Minimum0
Maximum21
Zeros104
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:36.035320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5555557
median1
Q31.2857123
95-th percentile3.3333333
Maximum21
Range21
Interquartile range (IQR)0.7301566

Descriptive statistics

Standard deviation1.39625
Coefficient of variation (CV)1.1395826
Kurtosis47.01528
Mean1.2252294
Median Absolute Deviation (MAD)0.4
Skewness5.3500323
Sum1805.9881
Variance1.9495141
MonotonicityNot monotonic
2024-04-03T01:30:36.264299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 380
25.8%
0.5 138
 
9.4%
0 104
 
7.1%
0.5555557 82
 
5.6%
0.8 49
 
3.3%
0.75 41
 
2.8%
0.85712287 38
 
2.6%
0.8333333 22
 
1.5%
0.33333334 22
 
1.5%
1.5 21
 
1.4%
Other values (280) 577
39.1%
ValueCountFrequency (%)
0 104
7.1%
0.11 3
 
0.2%
0.11011011 9
 
0.6%
0.110475111 1
 
0.1%
0.110525 1
 
0.1%
0.11135557 3
 
0.2%
0.11210755 3
 
0.2%
0.11244444 3
 
0.2%
0.112607511 5
 
0.3%
0.112735844 4
 
0.3%
ValueCountFrequency (%)
21 1
0.1%
13.3584111 2
0.1%
12 1
0.1%
11.833333 1
0.1%
11.5 1
0.1%
11 1
0.1%
10.857122 1
0.1%
10.333333 1
0.1%
8 2
0.1%
7.875 1
0.1%

BUGS
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)1.5%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.2571235
Minimum0
Maximum52
Zeros892
Zeros (%)60.5%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2024-04-03T01:30:36.496064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum52
Range52
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.4157049
Coefficient of variation (CV)2.7170799
Kurtosis69.047825
Mean1.2571235
Median Absolute Deviation (MAD)0
Skewness6.9719841
Sum1853
Variance11.66704
MonotonicityNot monotonic
2024-04-03T01:30:36.696495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 892
60.5%
1 238
 
16.1%
2 194
 
13.2%
3 36
 
2.4%
5 27
 
1.8%
4 20
 
1.4%
8 12
 
0.8%
7 11
 
0.7%
13 10
 
0.7%
10 9
 
0.6%
Other values (12) 25
 
1.7%
ValueCountFrequency (%)
0 892
60.5%
1 238
 
16.1%
2 194
 
13.2%
3 36
 
2.4%
4 20
 
1.4%
5 27
 
1.8%
7 11
 
0.7%
8 12
 
0.8%
10 9
 
0.6%
11 5
 
0.3%
ValueCountFrequency (%)
52 1
 
0.1%
40 1
 
0.1%
37 1
 
0.1%
35 1
 
0.1%
31 1
 
0.1%
24 2
0.1%
21 3
0.2%
20 1
 
0.1%
18 2
0.1%
17 2
0.1%

Interactions

2024-04-03T01:30:27.835164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:15.891388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.598290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.271855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.982940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.761078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:20.267262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:22.214843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:24.195323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:26.041265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:28.020355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:15.967880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.657794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.330008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.051419image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.823350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:20.443233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:22.421851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:24.357041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:26.204944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:28.230479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.026077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.722059image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.393865image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.140800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.901527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:20.580559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:22.651512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:24.515133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:26.363238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:28.441760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.084081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.781384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.464996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.214828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.990050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:20.987581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:22.874414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:24.688855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:26.488311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:28.722551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.161418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.874147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.565545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.286590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:19.175532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:21.186702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:23.086560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:24.861849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:26.681462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:28.895740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.231958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.947661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.632906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.363849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:19.405824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:21.346706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:23.254698image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:25.034360image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:26.921045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:29.066766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.297961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.016807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.706893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.433024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:19.569364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:21.481731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:23.471190image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:25.275299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:27.223277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:29.309312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.364474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.079661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.777813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.520624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:19.725853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:21.654186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:23.620797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:25.467930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:27.385091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:29.466439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.448712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.144285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.851506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.603855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:19.921199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:21.838844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:23.795964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:25.701061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:27.544921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:29.585186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:16.519842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.201284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:17.914678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:18.690007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:20.076194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:21.993870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:24.031404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:25.874701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-03T01:30:27.684180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-04-03T01:30:29.864982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-03T01:30:30.185441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SystemWMCDITNOCCBOLCOMfan-infan-outLOCMAXCCAVGCCBUGS
0ant5.01.02.05.013.03.03.0127.012.03.3333331.0
1ant52.01.00.027.0753.010.017.02272.05.01.3225811.0
2ant4.01.00.02.05.01.01.04.01.01.0000000.0
3ant3.01.00.00.03.00.00.03.01.01.0000002.0
4ant1.01.00.00.00.00.00.08.00.00.0000000.0
5ant22.01.00.00.0261.00.00.022.01.01.0000000.0
6ant4.01.00.00.05.00.00.04.01.01.0000000.0
7ant13.04.00.02.0105.00.02.0125.03.01.1333330.0
8ant2.01.00.02.01.01.01.02.01.01.0000000.0
9ant2.01.00.03.01.00.03.010.01.00.5000000.0
SystemWMCDITNOCCBOLCOMfan-infan-outLOCMAXCCAVGCCBUGS
1465ant3.02.00.02.00.01.01.025.02.01.0000000.0
1466ant12.04.00.08.055.00.08.0130.02.01.0000000.0
1467ant4.01.00.02.00.00.02.0111.01.00.7500000.0
1468ant12.01.00.04.081.01.03.0260.05.01.5712280.0
1469ant4.01.00.08.05.07.01.013.01.00.7500000.0
1470ant3.02.00.02.03.01.01.030.01.00.3333330.0
1471ant34.03.00.011.0435.00.011.01020.045.03.0588261.0
1472ant7.02.01.087.00.082.05.070.02.01.0000000.0
1473ant42.01.00.025.0553.05.0111.01212.040.02.2511102.0
1474------WebKitFormBoundaryIaolhmaqDHSCmiac--NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

SystemWMCDITNOCCBOLCOMfan-infan-outLOCMAXCCAVGCCBUGS# duplicates
24ant2.01.00.00.01.00.00.02.01.01.0000000.024
62ant4.01.00.00.05.00.00.04.01.01.0000000.012
28ant2.01.00.02.01.01.01.02.01.01.0000001.011
92ant7.04.00.03.021.00.03.0311.01.00.8571232.011
31ant2.01.00.04.00.01.03.028.01.00.5000000.010
49ant3.01.00.00.03.00.00.03.01.01.0000000.010
7ant1.01.00.00.00.00.00.01.01.01.0000000.09
64ant4.01.00.02.05.01.01.04.01.01.0000001.08
73ant5.01.00.00.013.00.00.05.01.01.0000000.08
27ant2.01.00.02.01.01.01.02.01.01.0000000.07